{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:IZPQXTB4ZO7UVROWMSLVLUVZJ5","short_pith_number":"pith:IZPQXTB4","schema_version":"1.0","canonical_sha256":"465f0bcc3ccbbf4ac5d6649755d2b94f4ea46cd35f5f37e0a273ad8de839023a","source":{"kind":"arxiv","id":"2309.14304","version":1},"attestation_state":"computed","paper":{"title":"Overview of Class Activation Maps for Visualization Explainability","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Anh Pham Thi Minh","submitted_at":"2023-09-25T17:20:51Z","abstract_excerpt":"Recent research in deep learning methodology has led to a variety of complex modelling techniques in computer vision (CV) that reach or even outperform human performance. Although these black-box deep learning models have obtained astounding results, they are limited in their interpretability and transparency which are critical to take learning machines to the next step to include them in sensitive decision-support systems involving human supervision. Hence, the development of explainable techniques for computer vision (XCV) has recently attracted increasing attention. In the realm of XCV, Cla"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2309.14304","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-09-25T17:20:51Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"64fc7189778cd07cbe4ffc9b3aa6a1f3e01a09158abc86777dd6a33d6ad7605d","abstract_canon_sha256":"8833ee9d4ff85c114ddb2539fbed06569fa0e400ecf57ce8121ea2e7cd3fbc74"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:54:09.436088Z","signature_b64":"15qBY1NhlbU1D77JBrEdCZMwqLPAXlf3sSIsG6ZftSa/lJx9PsgWvCP1YL+xMz3ficNWtBPxfSIgE0ZvymIVDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"465f0bcc3ccbbf4ac5d6649755d2b94f4ea46cd35f5f37e0a273ad8de839023a","last_reissued_at":"2026-07-05T06:54:09.435649Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:54:09.435649Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Overview of Class Activation Maps for Visualization Explainability","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Anh Pham Thi Minh","submitted_at":"2023-09-25T17:20:51Z","abstract_excerpt":"Recent research in deep learning methodology has led to a variety of complex modelling techniques in computer vision (CV) that reach or even outperform human performance. Although these black-box deep learning models have obtained astounding results, they are limited in their interpretability and transparency which are critical to take learning machines to the next step to include them in sensitive decision-support systems involving human supervision. Hence, the development of explainable techniques for computer vision (XCV) has recently attracted increasing attention. In the realm of XCV, Cla"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2309.14304","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2309.14304/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2309.14304","created_at":"2026-07-05T06:54:09.435708+00:00"},{"alias_kind":"arxiv_version","alias_value":"2309.14304v1","created_at":"2026-07-05T06:54:09.435708+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2309.14304","created_at":"2026-07-05T06:54:09.435708+00:00"},{"alias_kind":"pith_short_12","alias_value":"IZPQXTB4ZO7U","created_at":"2026-07-05T06:54:09.435708+00:00"},{"alias_kind":"pith_short_16","alias_value":"IZPQXTB4ZO7UVROW","created_at":"2026-07-05T06:54:09.435708+00:00"},{"alias_kind":"pith_short_8","alias_value":"IZPQXTB4","created_at":"2026-07-05T06:54:09.435708+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2501.06261","citing_title":"CAMs as Shapley Value-based Explainers","ref_index":18,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/IZPQXTB4ZO7UVROWMSLVLUVZJ5","json":"https://pith.science/pith/IZPQXTB4ZO7UVROWMSLVLUVZJ5.json","graph_json":"https://pith.science/api/pith-number/IZPQXTB4ZO7UVROWMSLVLUVZJ5/graph.json","events_json":"https://pith.science/api/pith-number/IZPQXTB4ZO7UVROWMSLVLUVZJ5/events.json","paper":"https://pith.science/paper/IZPQXTB4"},"agent_actions":{"view_html":"https://pith.science/pith/IZPQXTB4ZO7UVROWMSLVLUVZJ5","download_json":"https://pith.science/pith/IZPQXTB4ZO7UVROWMSLVLUVZJ5.json","view_paper":"https://pith.science/paper/IZPQXTB4","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2309.14304&json=true","fetch_graph":"https://pith.science/api/pith-number/IZPQXTB4ZO7UVROWMSLVLUVZJ5/graph.json","fetch_events":"https://pith.science/api/pith-number/IZPQXTB4ZO7UVROWMSLVLUVZJ5/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/IZPQXTB4ZO7UVROWMSLVLUVZJ5/action/timestamp_anchor","attest_storage":"https://pith.science/pith/IZPQXTB4ZO7UVROWMSLVLUVZJ5/action/storage_attestation","attest_author":"https://pith.science/pith/IZPQXTB4ZO7UVROWMSLVLUVZJ5/action/author_attestation","sign_citation":"https://pith.science/pith/IZPQXTB4ZO7UVROWMSLVLUVZJ5/action/citation_signature","submit_replication":"https://pith.science/pith/IZPQXTB4ZO7UVROWMSLVLUVZJ5/action/replication_record"}},"created_at":"2026-07-05T06:54:09.435708+00:00","updated_at":"2026-07-05T06:54:09.435708+00:00"}